from __future__ import annotations

# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Request scheduler policy"""

import os
import random
from collections import defaultdict
from contextlib import contextmanager
from enum import Enum, auto
from typing import TYPE_CHECKING, Dict, List, Optional, Set, Union

import torch

from sglang.srt.layers.attention.nsa.utils import is_nsa_enable_prefill_cp
from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
from sglang.srt.mem_cache.mamba_radix_cache import MambaRadixCache
from sglang.srt.mem_cache.radix_cache import RadixCache, RadixKey, TreeNode
from sglang.srt.server_args import ServerArgs

if TYPE_CHECKING:
    from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator

# Clip the estimation of max_new_tokens for the request whose max_new_tokens is very large.
# This can prevent the server from being too conservative.
# Note that this only clips the estimation in the scheduler but does not change the stop
# condition. The request can still generate tokens until it hits the unclipped max_new_tokens.
CLIP_MAX_NEW_TOKENS = int(
    os.environ.get("SGLANG_CLIP_MAX_NEW_TOKENS_ESTIMATION", "4096")
)

# Threshold for in-batch prefix cache.
# If a request has a matched prefix length (against existing cache) less than this value,
# the scheduler runs the in-batch prefix caching check for this request.
# If we set it to -1, it means we disable in-batch prefix caching.
IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD = int(
    os.environ.get("IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD", "32")
)

# Threshold for in-batch prefix cache.
# If a request has a matched prefix length (within the waiting queue) larger than this value,
# the scheduler deprioritizes this request
IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD = int(
    os.environ.get("IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD", "32")
)


IGNORE_EOS_RESERVE_TOKENS = 1


class CacheAwarePolicy(Enum):
    """Scheduling policies that are aware of the tree cache."""

    LPM = "lpm"  # longest prefix match
    DFS_WEIGHT = "dfs-weight"  # depth-first search weighting


class CacheAgnosticPolicy(Enum):
    """Scheduling policies that are not aware of the tree cache."""

    FCFS = "fcfs"  # first come first serve
    LOF = "lof"  # longest output first
    RANDOM = "random"


class SchedulePolicy:
    Policy = Union[CacheAwarePolicy, CacheAgnosticPolicy]

    def __init__(
        self,
        policy: str,
        tree_cache: BasePrefixCache,
        enable_hierarchical_cache: bool,
        enable_priority_scheduling: bool,
        schedule_low_priority_values_first: bool,
    ):
        self.policy = self._validate_and_adjust_policy(policy, tree_cache)
        self.tree_cache = tree_cache
        self.enable_hierarchical_cache = enable_hierarchical_cache
        self.enable_priority_scheduling = enable_priority_scheduling
        self.schedule_low_priority_values_first = schedule_low_priority_values_first

        # It is used to find the matching prefix for in-batch prefix caching.
        self.waiting_queue_radix_tree = RadixCache.create_simulated()

    def calc_priority(self, waiting_queue: List[Req]) -> bool:
        if self.policy == CacheAgnosticPolicy.FCFS:
            if self.enable_priority_scheduling:
                SchedulePolicy._sort_by_priority_and_fcfs(
                    waiting_queue, self.schedule_low_priority_values_first
                )
            return False

        policy = self._determine_active_policy(waiting_queue)

        prefix_computed = False
        if isinstance(policy, CacheAwarePolicy):
            prefix_computed = True
            temporary_deprioritized = self._compute_prefix_matches(
                waiting_queue, policy
            )
            if policy == CacheAwarePolicy.LPM:
                SchedulePolicy._sort_by_longest_prefix(
                    waiting_queue, temporary_deprioritized
                )
            elif policy == CacheAwarePolicy.DFS_WEIGHT:
                SchedulePolicy._sort_by_dfs_weight(waiting_queue, self.tree_cache)
            else:
                raise ValueError(f"Unknown CacheAware Policy: {policy=}")
        else:
            if policy == CacheAgnosticPolicy.FCFS:
                pass
            elif policy == CacheAgnosticPolicy.LOF:
                SchedulePolicy._sort_by_longest_output(
                    waiting_queue,
                    self.enable_priority_scheduling,
                    self.schedule_low_priority_values_first,
                )
            elif policy == CacheAgnosticPolicy.RANDOM:
                SchedulePolicy._sort_randomly(waiting_queue)
            else:
                raise ValueError(f"Unknown CacheAgnostic Policy: {policy=}")
        return prefix_computed

    def _determine_active_policy(self, waiting_queue: List[Req]) -> Policy:
        if self.policy == CacheAwarePolicy.LPM and len(waiting_queue) > 128:
            # Turn off the expensive prefix matching and sorting when the #queue is large.
            return CacheAgnosticPolicy.FCFS
        return self.policy

    def _validate_and_adjust_policy(
        self, policy: str, tree_cache: BasePrefixCache
    ) -> Policy:
        """
        Validates the policy and adjusts it if necessary based on tree cache settings.
        """
        try:
            policy_enum = CacheAwarePolicy(policy)
            if getattr(tree_cache, "disable", True):
                # If tree_cache is disabled, using CacheAgnosticPolicy policy
                return CacheAgnosticPolicy.FCFS
            return policy_enum
        except ValueError:
            try:
                return CacheAgnosticPolicy(policy)
            except ValueError:
                raise ValueError(f"Unknown schedule_policy: {policy=}")

    def _compute_prefix_matches(
        self, waiting_queue: List[Req], policy: CacheAwarePolicy
    ) -> Set[int]:
        """
        Computes and caches the matching prefixes for requests in the waiting queue,
            and handles in-batch prefix caching logic.
        """
        temporary_deprioritized: Set[int] = set()
        self.waiting_queue_radix_tree.reset()

        for r in waiting_queue:
            prefix_ids = r.origin_input_ids + r.output_ids
            extra_key = r.extra_key

            # NOTE: the prefix_indices must always be aligned with last_node
            match_result = self.tree_cache.match_prefix(
                rid=r.rid, key=RadixKey(token_ids=prefix_ids, extra_key=extra_key)
            )
            (
                r.prefix_indices,
                r.last_node,
                r.last_host_node,
                r.host_hit_length,
            ) = (
                match_result.device_indices,
                match_result.last_device_node,
                match_result.last_host_node,
                match_result.host_hit_length,
            )

            # NOTE(sang): This logic is for in-batch prefix caching;
            # If there are more than 1 request that have small matching prefix from
            # existing cache, but all those requests share the same prefix, we prefer
            # to schedule only one of them so that we can increase the cache hit rate.
            # We prefer to set IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD > 0 because too small
            # threshold means we cannot use in-batch prefix caching for short prefixes.
            # It is kind of common when the engine is long running (e.g., imagine the prefix "the").
            if len(r.prefix_indices) <= IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD:
                match_result = self.waiting_queue_radix_tree.match_prefix(
                    rid=r.rid,
                    key=RadixKey(token_ids=prefix_ids, extra_key=extra_key),
                )
                in_batch_matching_prefixes = match_result.device_indices
                if (
                    len(in_batch_matching_prefixes)
                    >= IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD
                ):
                    temporary_deprioritized.add(r.rid)
                else:
                    # Insert with a dummy key
                    self.waiting_queue_radix_tree.insert(
                        RadixKey(token_ids=prefix_ids, extra_key=extra_key),
                        torch.empty(len(prefix_ids), dtype=torch.bool),
                    )
        return temporary_deprioritized

    @staticmethod
    def _sort_by_longest_prefix(
        waiting_queue: List[Req], temporary_deprioritized: Set[int]
    ) -> None:
        """Sorts the waiting queue based on the longest prefix match."""
        waiting_queue.sort(
            key=lambda r: (
                -len(r.prefix_indices)
                if r.rid not in temporary_deprioritized
                else float("inf")
            )
        )

    @staticmethod
    def _sort_by_dfs_weight(
        waiting_queue: List[Req], tree_cache: BasePrefixCache
    ) -> None:
        """Sorts the waiting queue based on a depth-first search weighting."""
        last_node_to_reqs = defaultdict(list)
        for req in waiting_queue:
            last_node_to_reqs[req.last_node].append(req)

        node_to_weight = defaultdict(int)
        for node in last_node_to_reqs:
            node_to_weight[node] = len(last_node_to_reqs[node])
        SchedulePolicy._calc_weight(tree_cache.root_node, node_to_weight)

        waiting_queue.clear()
        SchedulePolicy._get_dfs_priority(
            tree_cache.root_node,
            node_to_weight,
            last_node_to_reqs,
            waiting_queue,
        )

    @staticmethod
    def _sort_by_longest_output(
        waiting_queue: List[Req],
        enable_priority_scheduling: bool,
        schedule_low_priority_values_first: bool,
    ) -> None:
        """Sorts the waiting queue based on the longest output (max_new_tokens). If using priority scheduling, sort by priority first."""
        if enable_priority_scheduling:
            if schedule_low_priority_values_first:
                waiting_queue.sort(
                    key=lambda x: (x.priority, -x.sampling_params.max_new_tokens)
                )
            else:
                waiting_queue.sort(
                    key=lambda x: (-x.priority, -x.sampling_params.max_new_tokens)
                )
        else:
            waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)

    @staticmethod
    def _sort_randomly(waiting_queue: List[Req]) -> None:
        """Shuffles the waiting queue randomly."""
        random.shuffle(waiting_queue)

    @staticmethod
    def _sort_by_priority_and_fcfs(
        waiting_queue: List[Req], schedule_low_priority_values_first: bool
    ) -> None:
        """Sorts the waiting queue based on the request priority then received titmestamp."""
        if schedule_low_priority_values_first:
            waiting_queue.sort(
                key=lambda x: (x.priority, x.time_stats.wait_queue_entry_time)
            )
        else:
            waiting_queue.sort(
                key=lambda x: (-x.priority, x.time_stats.wait_queue_entry_time)
            )

    @staticmethod
    def _calc_weight(cur_node: TreeNode, node_to_weight: Dict[TreeNode, int]) -> None:
        for child in cur_node.children.values():
            SchedulePolicy._calc_weight(child, node_to_weight)
            node_to_weight[cur_node] += node_to_weight[child]

    @staticmethod
    def _get_dfs_priority(
        cur_node: TreeNode,
        node_to_priority: Dict[TreeNode, int],
        last_node_to_reqs: Dict[TreeNode, List[Req]],
        q: List,
    ) -> None:
        childs = [child for child in cur_node.children.values()]
        childs.sort(key=lambda x: -node_to_priority[x])
        for child in childs:
            SchedulePolicy._get_dfs_priority(
                child, node_to_priority, last_node_to_reqs, q
            )
        q.extend(last_node_to_reqs[cur_node])


class AddReqResult(Enum):
    CONTINUE = auto()  # Continue to add requests
    NO_TOKEN = auto()  # No token left
    OTHER = auto()  # Other reasons to stop adding requests


class PrefillAdder:
    def __init__(
        self,
        page_size: int,
        tree_cache: BasePrefixCache,
        token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator,
        running_batch: ScheduleBatch,
        new_token_ratio: float,
        rem_input_tokens: int,
        rem_chunk_tokens: Optional[int],
        mixed_with_decode_tokens: int = 0,
        priority_scheduling_preemption_threshold: int = 0,
    ):
        self.page_size = page_size
        self.tree_cache = tree_cache
        self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
        self.running_batch = running_batch
        self.new_token_ratio = new_token_ratio
        self.rem_input_tokens = rem_input_tokens - mixed_with_decode_tokens
        self.rem_chunk_tokens = rem_chunk_tokens
        if self.rem_chunk_tokens is not None:
            self.rem_chunk_tokens -= mixed_with_decode_tokens

        self.rem_total_token_offset = mixed_with_decode_tokens
        self.cur_rem_token_offset = mixed_with_decode_tokens

        self.req_states = None
        self.can_run_list = []
        self.preempt_list = []
        self.new_chunked_req = None
        self.log_hit_tokens = 0
        # TODO(lsyin): report the real input tokens excluding page alignment
        self.log_input_tokens = 0

        if running_batch is not None:
            self.rem_total_token_offset += sum(
                [
                    self._get_running_request_total_token_offset(r)
                    for r in running_batch.reqs
                ]
            )

        self.is_hybrid_swa = isinstance(
            self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
        )
        self.is_ssm_radix_cache = isinstance(self.tree_cache, MambaRadixCache)

        self.priority_scheduling_preemption_threshold = (
            priority_scheduling_preemption_threshold
        )
        self.nsa_enable_prefill_cp = is_nsa_enable_prefill_cp()

    def _get_running_request_total_token_offset(self, req: Req) -> int:
        return (
            min(
                (req.sampling_params.max_new_tokens - len(req.output_ids)),
                CLIP_MAX_NEW_TOKENS,
            )
            * self.new_token_ratio
        )

    @property
    def rem_total_tokens(self):
        if self.is_hybrid_swa:
            available_and_evictable = min(
                self.token_to_kv_pool_allocator.full_available_size()
                + self.tree_cache.full_evictable_size(),
                self.token_to_kv_pool_allocator.swa_available_size()
                + self.tree_cache.swa_evictable_size(),
            )
        elif self.is_ssm_radix_cache:
            available_and_evictable = (
                self.token_to_kv_pool_allocator.available_size()
                + self.tree_cache.full_evictable_size()
            )
        else:
            available_and_evictable = (
                self.token_to_kv_pool_allocator.available_size()
                + self.tree_cache.evictable_size()
            )

        return available_and_evictable - self.rem_total_token_offset

    @property
    def cur_rem_tokens(self):
        if self.is_hybrid_swa:
            available_and_evictable = min(
                self.token_to_kv_pool_allocator.full_available_size()
                + self.tree_cache.full_evictable_size(),
                self.token_to_kv_pool_allocator.swa_available_size()
                + self.tree_cache.swa_evictable_size(),
            )
        elif self.is_ssm_radix_cache:
            available_and_evictable = (
                self.token_to_kv_pool_allocator.available_size()
                + self.tree_cache.full_evictable_size()
            )
        else:
            available_and_evictable = (
                self.token_to_kv_pool_allocator.available_size()
                + self.tree_cache.evictable_size()
            )

        return available_and_evictable - self.cur_rem_token_offset

    def ceil_paged_tokens(self, tokens: int) -> int:
        return -(-tokens // self.page_size) * self.page_size

    def budget_state(self):
        if self.rem_total_tokens <= 0 or self.cur_rem_tokens <= 0:
            return AddReqResult.NO_TOKEN

        if self.rem_input_tokens <= 0 or (
            self.rem_chunk_tokens is not None and self.rem_chunk_tokens <= 0
        ):
            return AddReqResult.OTHER

        return AddReqResult.CONTINUE

    def _update_prefill_budget(
        self, prefix_len: int, extend_input_len: int, max_new_tokens: int
    ):
        # TODO(lsyin): check this workaround logic, which only ensures the prefill will not out of memory, and may be too conservative
        extend_input_len = self.ceil_paged_tokens(extend_input_len)

        self.rem_total_token_offset += extend_input_len + max_new_tokens
        self.cur_rem_token_offset += extend_input_len
        self.rem_input_tokens -= extend_input_len
        if self.rem_chunk_tokens is not None:
            self.rem_chunk_tokens -= extend_input_len

        self.log_hit_tokens += prefix_len
        self.log_input_tokens += extend_input_len

    def add_chunked_req(self, req: Req):
        _rem_tokens = min(self.rem_chunk_tokens, int(self.rem_total_tokens))
        truncated = req.extend_input_len > _rem_tokens
        req.extend_input_len = min(req.extend_input_len, _rem_tokens)
        req.fill_ids = req.fill_ids[: len(req.prefix_indices) + req.extend_input_len]
        self.can_run_list.append(req)
        self._update_prefill_budget(
            0,
            req.extend_input_len,
            (
                min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS)
                if not truncated
                else 0
            ),
        )

        # Return if chunked prefill not finished
        return req if truncated else None

    @contextmanager
    def _lock_node(self, last_node: TreeNode):
        if self.is_hybrid_swa:
            try:
                swa_uuid_for_lock = self.tree_cache.inc_lock_ref(last_node)
                yield None
            finally:
                self.tree_cache.dec_lock_ref(last_node, swa_uuid_for_lock)
        else:
            try:
                self.tree_cache.inc_lock_ref(last_node)
                yield None
            finally:
                self.tree_cache.dec_lock_ref(last_node)

    def add_one_req_ignore_eos(self, req: Req, has_chunked_req: bool):
        # Early exit if no enough tokens for the input tokens
        if self.ceil_paged_tokens(req.extend_input_len) > min(
            self.cur_rem_tokens, self.rem_total_tokens
        ):
            return AddReqResult.NO_TOKEN

        def add_req_state(r, insert_sort=False):
            new_token_ratio = (
                1.0 if r.sampling_params.ignore_eos else self.new_token_ratio
            )
            tokens_left = r.sampling_params.max_new_tokens * new_token_ratio - len(
                r.output_ids
            )
            tokens_occupied = len(r.origin_input_ids) + len(r.output_ids)

            if tokens_left <= 0:
                return

            if not insert_sort:
                self.req_states.append((tokens_left, tokens_occupied))
            else:
                i = 0
                for i in range(len(self.req_states)):
                    if tokens_left <= self.req_states[i][0]:
                        break
                self.req_states.insert(i, (tokens_left, tokens_occupied))

        if self.req_states is None:
            self.req_states = []
            add_req_state(req)
            if self.running_batch is not None:
                for r in self.running_batch.reqs:
                    add_req_state(r)
            for r in self.can_run_list:
                add_req_state(r)
            self.req_states.sort(key=lambda x: x[0])
        else:
            add_req_state(req, insert_sort=True)

        if not self.is_hybrid_swa:
            # Skip this logic for swa. The SWA has different memory management, and
            # this mechanism is underestimating the memory usage.
            cur_rem_tokens = self.cur_rem_tokens - self.ceil_paged_tokens(
                req.extend_input_len
            )
            tokens_freed = 0
            for i, (tokens_left, tokens_occupied) in enumerate(self.req_states):
                # tokens_left gives a reservative calculation as the last token is not stored
                bs = len(self.req_states) - i
                min_free_tokens = cur_rem_tokens + tokens_freed - tokens_left * bs
                # reserve tokens for corner cases
                if min_free_tokens <= IGNORE_EOS_RESERVE_TOKENS * bs:
                    return AddReqResult.NO_TOKEN
                tokens_freed += tokens_occupied

        if (
            self.rem_chunk_tokens is None  # chunked prefill is disabled
            or req.extend_input_len <= self.rem_chunk_tokens  # it is the last chunk
        ):
            # Non-chunked prefill
            self.can_run_list.append(req)
            self._update_prefill_budget(
                0,
                req.extend_input_len,
                min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS),
            )
        else:
            if self.rem_chunk_tokens <= 0:
                return AddReqResult.OTHER

            # Chunked prefill
            trunc_len = self.rem_chunk_tokens

            req.extend_input_len = trunc_len
            req.fill_ids = req.fill_ids[:trunc_len]
            self.can_run_list.append(req)
            self.new_chunked_req = req
            self._update_prefill_budget(0, trunc_len, 0)

        return self.budget_state()

    def add_one_req(
        self, req: Req, has_chunked_req: bool, truncation_align_size: Optional[int]
    ):
        # TODO support cp with multiple requests
        # Enabling context parallelism currently presents precision issues;
        # therefore, the prefill-batch setting is temporarily set to 1.
        if self.nsa_enable_prefill_cp and len(self.can_run_list) >= 1:
            return AddReqResult.OTHER
        if req.sampling_params.ignore_eos and getattr(self.tree_cache, "disable", True):
            return self.add_one_req_ignore_eos(req, has_chunked_req)

        total_tokens = req.extend_input_len + min(
            max(req.sampling_params.max_new_tokens - len(req.output_ids), 0),
            CLIP_MAX_NEW_TOKENS,
        )

        # adjusting the input_tokens based on host_hit_length and page_size
        real_input_tokens = req.extend_input_len - req.host_hit_length
        real_input_tokens = self.ceil_paged_tokens(real_input_tokens)
        prefix_len = len(req.prefix_indices)

        if total_tokens >= self.rem_total_tokens:
            return AddReqResult.NO_TOKEN

        if real_input_tokens >= self.rem_input_tokens and len(self.can_run_list) != 0:
            return AddReqResult.OTHER

        with self._lock_node(req.last_node):
            # self.rem_total_tokens may decrease after the lock acquisition
            if total_tokens >= self.rem_total_tokens:
                return AddReqResult.NO_TOKEN

            if req.host_hit_length > 0:
                new_indices, req.last_node = self.tree_cache.init_load_back(
                    req.last_host_node, req.host_hit_length
                )
                req.prefix_indices = torch.cat([req.prefix_indices, new_indices])
                req.extend_input_len = len(req.fill_ids) - len(req.prefix_indices)
                prefix_len = len(req.prefix_indices)
                req.cache_protected_len = prefix_len

            input_tokens = self.ceil_paged_tokens(req.extend_input_len)

            if input_tokens >= self.rem_input_tokens and len(self.can_run_list) != 0:
                return AddReqResult.OTHER

            if self.rem_chunk_tokens is None or input_tokens <= self.rem_chunk_tokens:
                # Non-chunked prefill
                self.can_run_list.append(req)
                if self.is_hybrid_swa:
                    swa_uuid_for_lock = self.tree_cache.inc_lock_ref(req.last_node)
                    req.swa_uuid_for_lock = swa_uuid_for_lock
                else:
                    self.tree_cache.inc_lock_ref(req.last_node)
                self._update_prefill_budget(
                    prefix_len,
                    input_tokens,
                    min(
                        req.sampling_params.max_new_tokens,
                        CLIP_MAX_NEW_TOKENS,
                    ),
                )
            else:
                # Make sure at least one page is available
                trunc_len = self.rem_chunk_tokens // self.page_size * self.page_size
                if trunc_len <= 0:
                    return AddReqResult.OTHER

                # When truncation align size is set, we want to assert that the prefill prefix length is multiple of truncation align size
                # A typical use case is when deterministic inference is enabled with flashinfer attention backend,
                # we need the prefill prefix length to be multiple of attention split size
                if truncation_align_size is not None:
                    if trunc_len < truncation_align_size:
                        return AddReqResult.OTHER
                    else:
                        trunc_len = truncation_align_size * (
                            trunc_len // truncation_align_size
                        )

                # Chunked prefill
                req.extend_input_len = trunc_len
                req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]

                self.can_run_list.append(req)
                self.new_chunked_req = req
                if self.is_hybrid_swa:
                    swa_uuid_for_lock = self.tree_cache.inc_lock_ref(req.last_node)
                    req.swa_uuid_for_lock = swa_uuid_for_lock
                else:
                    self.tree_cache.inc_lock_ref(req.last_node)
                self._update_prefill_budget(prefix_len, trunc_len, 0)

        return self.budget_state()

    def preempt_to_schedule(self, req: Req, server_args: ServerArgs) -> bool:
        """
        Preempt running requests to serve the new request if the priority threshold is met and token count sum is verified.
        Returns True if preemption was committed, and the new request can be scheduled.
        """
        # Iterate running requests to find preemptible requests
        valid_running_reqs = (
            r for r in self.running_batch.reqs if r not in self.preempt_list
        )
        if server_args.schedule_low_priority_values_first:
            sorted_valid_running_reqs = sorted(
                valid_running_reqs,
                key=lambda x: (-x.priority, -x.time_stats.wait_queue_entry_time),
            )
        else:
            sorted_valid_running_reqs = sorted(
                valid_running_reqs,
                key=lambda x: (x.priority, -x.time_stats.wait_queue_entry_time),
            )
        preemptible_reqs = []
        min_tokens_to_remove = (
            req.extend_input_len
            + min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS)
            - self.rem_total_tokens
        )
        for running_req in sorted_valid_running_reqs:
            # Priority difference needs to meet the threshold to be preemptible.
            priority_diff = req.priority - running_req.priority
            if server_args.schedule_low_priority_values_first:
                priority_diff *= -1
            if priority_diff > self.priority_scheduling_preemption_threshold:
                preemptible_reqs.append(running_req)
                min_tokens_to_remove -= self._get_running_request_total_token_offset(
                    running_req
                )
                if min_tokens_to_remove <= 0:
                    break
            else:
                break

        # Check max token count limit can be met
        if len(preemptible_reqs) == 0 or min_tokens_to_remove > 0:
            return False

        # Preempt running requests. Release allocated resources for immediate usage.
        preemptible_reqs = set(preemptible_reqs)
        keep_indices = []
        release_counter = 0
        for i, running_req in enumerate(self.running_batch.reqs):
            if running_req in preemptible_reqs:
                self.rem_total_token_offset -= (
                    self._get_running_request_total_token_offset(running_req)
                )
                release_counter += 1
                self.running_batch.release_req(
                    i, len(self.running_batch.reqs) - release_counter, server_args
                )
            else:
                keep_indices.append(i)
        self.running_batch.filter_batch(keep_indices=keep_indices)
        self.preempt_list.extend(preemptible_reqs)
        return True
